diff --git a/README.md b/README.md index d4e417f..c959275 100644 --- a/README.md +++ b/README.md @@ -1,22 +1,34 @@ # ScenarioNet -**Open-Source Platform for Large-Scale Traffic Scenario Simulation and Modeling** [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/metadriverse/scenarionet/blob/main/tutorial/simulation.ipynb) +**Open-Source Platform for Large-Scale Traffic Scenario Simulation and Modeling** [ [**Webpage**](https://metadriverse.github.io/scenarionet/) | [**Code**](https://github.com/metadriverse/scenarionet) | [**Video**](https://youtu.be/3bOqswXP6OA) | [**Paper**](http://arxiv.org/abs/2306.12241) | -[**Documentation**](https://scenarionet.readthedocs.io/en/latest/) | -[**Colab Example**](https://colab.research.google.com/github/metadriverse/scenarionet/blob/main/tutorial/simulation.ipynb) +[**Documentation**](https://scenarionet.readthedocs.io/en/latest/) ] -ScenarioNet allows users to load scenarios from real-world dataset like Waymo, nuPlan, nuScenes, l5 and synthetic -dataset such as procedural generated ones and safety-critical ones generated by adversarial attack. -The built database provides tools for building training and test sets for ML applications. -Powered by [MetaDrive Simulator](https://github.com/metadriverse/metadrive), the scenarios can be reconstructed for -various applications like AD stack test, reinforcement learning, imitation learning, scenario generation and so on. +***Colab example for running simulation with ScenarioNet:*** +[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/metadriverse/scenarionet/blob/main/tutorial/simulation.ipynb) + + +***Colab example for reading established ScenarioNet dataset:*** +[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/metadriverse/scenarionet/blob/main/tutorial/read_established_scenarionet_dataset.ipynb) + + + + +ScenarioNet allows users to load scenarios from real-world datasets like Waymo, nuPlan, +nuScenes, l5 and synthetic dataset such as procedural generated ones and safety-critical +ones generated by adversarial attack. The built database provides tools for building +training and test sets for ML applications. + +Powered by [MetaDrive Simulator](https://github.com/metadriverse/metadrive), +the scenarios can be reconstructed for various applications like AD stack test, +reinforcement learning, imitation learning, scenario generation and so on. ![system](docs/asset/system.png) @@ -32,11 +44,13 @@ conda create -n scenarionet python=3.9 conda activate scenarionet # Install MetaDrive Simulator -git clone git@github.com:metadriverse/metadrive.git +cd ~/ # Go to the folder you want to host these two repos. +git clone https://github.com/metadriverse/metadrive.git cd metadrive pip install -e. # Install ScenarioNet +cd ~/ # Go to the folder you want to host these two repos. git clone git@github.com:metadriverse/scenarionet.git cd scenarionet pip install -e . @@ -50,7 +64,7 @@ If you already have ScenarioNet installed, you can check all operations by `pyth ## Citation -If you used this project in your research, please cite +If you used this project in your research, please cite: ```latex @article{li2023scenarionet, @@ -58,5 +72,5 @@ If you used this project in your research, please cite author={Li, Quanyi and Peng, Zhenghao and Feng, Lan and Duan, Chenda and Mo, Wenjie and Zhou, Bolei and others}, journal={arXiv preprint arXiv:2306.12241}, year={2023} - } +} ``` diff --git a/documentation/README.md b/documentation/README.md index cdd3a05..f2ab02e 100644 --- a/documentation/README.md +++ b/documentation/README.md @@ -1,9 +1,11 @@ -This folder contains files for the documentation: [https://scenarionet.readthedocs.io/](https://scenarionet.readthedocs.io/). +This folder contains files for the documentation: +[https://scenarionet.readthedocs.io/](https://scenarionet.readthedocs.io/). -To build documents locally, please run the following codes: +To build documents locally, please run the following script: -``` +```bash +cd scenarionet pip install -e .[doc] -cd scenarionet/documentation +cd documentation make html ``` diff --git a/documentation/build.sh b/documentation/build.sh new file mode 100644 index 0000000..ec7299c --- /dev/null +++ b/documentation/build.sh @@ -0,0 +1,2 @@ +rm -rf build +make html \ No newline at end of file diff --git a/documentation/example.rst b/documentation/example.rst index 3e1b968..b8f0b14 100644 --- a/documentation/example.rst +++ b/documentation/example.rst @@ -1,25 +1,45 @@ +##################### +Example Colab +##################### + + +.. |colab_sim| image:: https://colab.research.google.com/assets/colab-badge.svg + :alt: Open In Colab + :target: https://colab.research.google.com/github/metadriverse/scenarionet/blob/main/tutorial/simulation.ipynb + + +**Colab example for running simulation with ScenarioNet:** |colab_sim| + + +.. |colab_read| image:: https://colab.research.google.com/assets/colab-badge.svg + :alt: Open In Colab + :target: https://colab.research.google.com/github/metadriverse/scenarionet/blob/main/tutorial/read_established_scenarionet_dataset.ipynb + +**Colab example for reading established ScenarioNet dataset:** |colab_read| + + + ####################### Waymo Example ####################### -In this example, we will show you how to convert a small batch of `Waymo `_ scenarios into the internal Scenario Description. -After that, the scenarios will be loaded to simulator for closed-loop simulation. +In this example, we will show you how to convert a small batch of `Waymo `_ scenarios into the internal **Scenario Description**. +After that, the scenarios will be loaded to MetaDrive simulator for closed-loop simulation. First of all, please install `MetaDrive `_ and `ScenarioNet `_ following these steps :ref:`installation`. + 1. Setup Waymo toolkit ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ -For any dataset, this step is necessary after installing ScenarioNet, -as we need to use the official toolkits of the data provider to parse the original scenario description and convert to our internal scenario description. -For Waymo data, please install the toolkit via:: +For any dataset, the first step after installing ScenarioNet is to install the corresponding official toolkit as we need to use it to parse the original data and convert to our internal scenario description. For Waymo data, please install the toolkit via:: pip install waymo-open-dataset-tf-2-11-0 pip install tensorflow==2.11.0 .. note:: This package is only supported on Linux platform. - `waymo-open-dataset` may degrade numpy, causing conflicts with cv2. - A workaround is ``pip install numpy==1.24.2`` + `waymo-open-dataset` may degrade numpy, causing conflicts with `cv2` (`opencv-python`). + A workaround is to ``pip install numpy==1.24.2`` For other datasets like nuPlan and nuScenes, you need to setup `nuplan-devkit `_ and `nuscenes-devkit `_ respectively. Guidance on how to setup these datasets and connect them with ScenarioNet can be found at :ref:`datasets`. diff --git a/documentation/index.rst b/documentation/index.rst index 5cd454b..529663d 100644 --- a/documentation/index.rst +++ b/documentation/index.rst @@ -3,6 +3,24 @@ ScenarioNet Documentation ########################## + +.. |colab_sim| image:: https://colab.research.google.com/assets/colab-badge.svg + :alt: Open In Colab + :target: https://colab.research.google.com/github/metadriverse/scenarionet/blob/main/tutorial/simulation.ipynb + + +**Colab example for running simulation with ScenarioNet:** |colab_sim| + + +.. |colab_read| image:: https://colab.research.google.com/assets/colab-badge.svg + :alt: Open In Colab + :target: https://colab.research.google.com/github/metadriverse/scenarionet/blob/main/tutorial/read_established_scenarionet_dataset.ipynb + +**Colab example for reading established ScenarioNet dataset:** |colab_read| + + + + Welcome to the ScenarioNet documentation! ScenarioNet is an open-sourced platform for large-scale traffic scenario modeling and simulation with the following features: @@ -14,7 +32,7 @@ It can thus support several applications including large-scale scenario generati This documentation brings you the information on installation, usages and more of ScenarioNet! You can also visit the `GitHub repo `_ and `Webpage `_ for code and videos. -Please feel free to contact us if you have any suggestions or ideas! +Please feel free to contact us if you have any suggestion or idea! .. toctree:: @@ -23,12 +41,12 @@ Please feel free to contact us if you have any suggestions or ideas! install.rst example.rst - operations.rst + .. modify the toctree in datasets.rst together .. toctree:: :maxdepth: 1 - :caption: Supported Dataset + :caption: Setup Datasets datasets.rst nuplan.rst @@ -38,6 +56,14 @@ Please feel free to contact us if you have any suggestions or ideas! lyft.rst new_data.rst + +.. toctree:: + :maxdepth: 2 + :caption: Operations + + operations.rst + + .. toctree:: :maxdepth: 2 :caption: System Design diff --git a/documentation/install.rst b/documentation/install.rst index d820df9..c267cf1 100644 --- a/documentation/install.rst +++ b/documentation/install.rst @@ -19,9 +19,9 @@ We recommend to create a new conda environment and install Python>=3.8,<=3.9:: In addition, the operations in ScenarioNet are executed as Python module ``python -m``, and thus we have to make sure the working directory contains NO folders named ``metadrive`` or ``scenarionet``. -Therefore, we strongly recommend creating a new folder under your routine working directory. +Therefore, we strongly recommend creating a new folder under your daily working directory. For example, supposing you prefer working at ``/home/lee``, -it would be greate to have a new folder ``mdsn`` created under this path. +it would be great to have a new folder ``mdsn`` created under this path. And the ``git clone`` and package installation should happen in this new directory. As a result, the directory tree should look like this:: @@ -32,7 +32,7 @@ As a result, the directory tree should look like this:: ├──... In this way, you can freely run the dataset operations at any places other than ``/home/lee/mdsn``. -Now, let's move to this new directory for further installation with ``cd mdsn``. +Now, let's move current workding directory to this new directory for further installation with ``cd mdsn``. .. note:: This step is optional. One can still ``git clone`` and ``pip install`` the following two packages at any places. @@ -42,15 +42,21 @@ Now, let's move to this new directory for further installation with ``cd mdsn``. 3. Install MetaDrive ~~~~~~~~~~~~~~~~~~~~~~~~~~~~ -The simulation part is maintained in `MetaDrive `_ repo, and let's install MetaDrive first. +The simulation part is maintained in `MetaDrive `_ repo, +and let's install MetaDrive first. The installation of MetaDrive on different platforms is straightforward and easy! We recommend to install in the following ways:: - # Method 1 (Recommend, latest version, source code exposed) + # Method 1A (Recommended, latest version, source code exposed) git clone git@github.com:metadriverse/metadrive.git cd metadrive pip install -e. + # Method 1B (Recommended, latest version, source code exposed) + git clone https://github.com/metadriverse/metadrive.git + cd metadrive + pip install -e. + # Method 2 (Stable version, source code hidden) pip install "metadrive-simulator>=0.4.1.1" @@ -58,20 +64,18 @@ To check whether MetaDrive is successfully installed, please run:: python -m metadrive.examples.profile_metadrive -.. note:: Please do not run the above command at a directory that has a sub-folder called :code:`./metadrive`. +.. note:: Please do not run the above command at a directory that has a subfolder called :code:`./metadrive`. 4. Install ScenarioNet ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ -For ScenarioNet, we only provide Github installation:: +For ScenarioNet, we only provide the Github installation:: git clone git@github.com:metadriverse/scenarionet.git cd scenarionet pip install -e . .. note:: - If you don't wanna access the source code, you can install these two packages with + If you don't want to access the source code, you can install these two packages with ``pip install git+https://github.com/metadriverse/scenarionet.git`` and ``pip install git+https://github.com/metadriverse/metadrive.git``. - Though it is more straightforward, one has to install additional requirements, like development - toolkits, manually. diff --git a/documentation/operations.rst b/documentation/operations.rst index 3fc37d7..7e7ac5a 100644 --- a/documentation/operations.rst +++ b/documentation/operations.rst @@ -1,6 +1,6 @@ -########### +###################### Operations -########### +###################### How to run ~~~~~~~~~~ diff --git a/scenarionet/__init__.py b/scenarionet/__init__.py index 951bfef..9545f30 100644 --- a/scenarionet/__init__.py +++ b/scenarionet/__init__.py @@ -1,5 +1,7 @@ import os +from scenarionet.common_utils import read_dataset_summary, read_scenario + SCENARIONET_PACKAGE_PATH = os.path.dirname(os.path.abspath(__file__)) SCENARIONET_REPO_PATH = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) SCENARIONET_DATASET_PATH = os.path.join(SCENARIONET_REPO_PATH, "dataset") diff --git a/scenarionet/common_utils.py b/scenarionet/common_utils.py index 664264c..68c2c70 100644 --- a/scenarionet/common_utils.py +++ b/scenarionet/common_utils.py @@ -78,15 +78,29 @@ def save_summary_anda_mapping(summary_file_path, mapping_file_path, summary, map def read_dataset_summary(dataset_path): + """Read the dataset and return the metadata of each scenario in this dataset. + + Args: + dataset_path: the path to the root folder of your dataset. + + Returns: + A tuple of three elements: + 1) the summary dict mapping from scenario ID to its metadata, + 2) the list of all scenarios IDs, and + 3) a dict mapping from scenario IDs to the folder that hosts their files. + """ return sd_utils.read_dataset_summary(dataset_path) def read_scenario(dataset_path, mapping, scenario_file_name): - """ - read a scenario - :param dataset_path: the location where dataset_summary.pkl is - :param mapping: a dict recording the relative position from dataset_path to real scenario file - :param scenario_file_name: scenario filename - :return: ScenarioDescription + """Read a scenario pkl file and return the Scenario Description instance. + + Args: + dataset_path: the path to the root folder of your dataset. + mapping: the dict mapping return from read_dataset_summary. + scenario_file_name: the file name to a scenario file, should end with `.pkl`. + + Returns: + The Scenario Description instance of that scenario. """ return sd_utils.read_scenario_data(os.path.join(dataset_path, mapping[scenario_file_name], scenario_file_name)) diff --git a/setup.py b/setup.py index 0ccf1e7..bcbb00b 100644 --- a/setup.py +++ b/setup.py @@ -1,4 +1,3 @@ -# Please don't change the order of following packages! import os import sys from os import path @@ -30,7 +29,7 @@ print("We will install the following packages: ", packages) version = "0.0.1" install_requires = [ - "numpy>=1.21.6", + "numpy>=1.23.0", "matplotlib", "pandas", "tqdm", @@ -53,9 +52,12 @@ train_requirement = [ "aiohttp==3.6.0", "gymnasium", "tensorflow", - "tensorflow_probability"] + "tensorflow_probability" +] -# Disable this kind of installation +# Remove the dependencies to real-world dataset. Instead, we will point the user to the installation guideline +# in the original sources. +# # waymo = ["waymo-open-dataset-tf-2-11-0", "tensorflow==2.11.0"] # # nuplan = ["nuplan-devkit>=1.2.0", diff --git a/tutorial/read_established_scenarionet_dataset.ipynb b/tutorial/read_established_scenarionet_dataset.ipynb new file mode 100644 index 0000000..a02d319 --- /dev/null +++ b/tutorial/read_established_scenarionet_dataset.ipynb @@ -0,0 +1,3714 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "J1w-zrxAUr4l", + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "# Read Established ScenarioNet Dataset\n", + "\n", + "Welcome to try out MetaDrive & ScenarioNet!\n", + "\n", + "For the researchers how focus on motion prediction, scenario generation, and more, you probably will not\n", + "use the interactive environment provided by MetaDrive.\n", + "\n", + "In this tutorial, we will navigate you through how to read established ScenarioNet dataset and give you a sense of the data format." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "2efvTXdHVptN", + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "## Installation\n", + "\n", + "Note that even we only need to load the data, you still need to install MetaDrive. But no worry, it's quite easy!" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "id": "65J2iQKpUQ1B", + "outputId": "60411b22-c699-4db0-bf23-13b4c83c4d4f", + "pycharm": { + "name": "#%%\n" + }, + "scrolled": true + }, + "outputs": [], + "source": [ + "#@title Collect the MetaDrive & ScenarioNet\n", + "# NOTE: If you are running this notebook locally with installtion finished, this step is not required.\n", + "RunningInCOLAB = 'google.colab' in str(get_ipython()) # Detect if it is running in Colab\n", + "if RunningInCOLAB:\n", + " %pip install git+https://github.com/metadriverse/metadrive.git\n", + " %pip install git+https://github.com/metadriverse/scenarionet.git" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next, let's create a 2D visualization tool for recording the scenario in GIF." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "pygame 2.5.2 (SDL 2.28.2, Python 3.9.18)\n", + "Hello from the pygame community. https://www.pygame.org/contribute.html\n" + ] + } + ], + "source": [ + "# visualization\n", + "from IPython.display import Image as IImage\n", + "import pygame\n", + "import numpy as np\n", + "from PIL import Image\n", + "\n", + "def make_GIF(frames, name=\"demo.gif\"):\n", + " print(\"Generate gif...\")\n", + " imgs = [pygame.surfarray.array3d(frame) for frame in frames]\n", + " imgs = [Image.fromarray(img) for img in imgs]\n", + " imgs[0].save(name, save_all=True, append_images=imgs[1:], duration=50, loop=0)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Configuration\n", + "\n", + "Let's import some modules and specify the dataset directory." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Fail to pull. Assets already exists, version: 0.4.1.2. Expected version: 0.4.1.2. To overwrite existing assets and update, add flag '--update' and rerun this script\n" + ] + } + ], + "source": [ + "#@title Make some configurations and import some modules\n", + "from metadrive.engine.engine_utils import close_engine\n", + "close_engine()\n", + "from metadrive.pull_asset import pull_asset\n", + "pull_asset(False)\n", + "# NOTE: usually you don't need the above lines. It is only for avoiding a potential bug when running on colab\n", + "\n", + "from metadrive.engine.asset_loader import AssetLoader\n", + "from metadrive.policy.replay_policy import ReplayEgoCarPolicy\n", + "from metadrive.envs.scenario_env import ScenarioEnv\n", + "import os\n", + "\n", + "os.environ[\"SDL_VIDEODRIVER\"] = \"dummy\" # Hide the pygame window" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We prepare two demo datasets, splitting from Waymo Open Dataset and nuScenes Dataset. Here we demonstrate the files structure:" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['sd_training.tfrecord-00000-of-01000_c403d5992cab9e0.pkl',\n", + " 'dataset_summary.pkl',\n", + " 'sd_training.tfrecord-00000-of-01000_2a1e44d405a6833f.pkl',\n", + " 'sd_training.tfrecord-00000-of-01000_8a346109094cd5aa.pkl']" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "waymo_data = AssetLoader.file_path(AssetLoader.asset_path, \"waymo\", return_raw_style=False) # Use the built-in datasets with simulator\n", + "os.listdir(waymo_data) # there are 3 waymo scenario file with a 'dataset_summary.pkl'" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['dataset_summary.pkl',\n", + " 'nuscenes_3',\n", + " 'nuscenes_6',\n", + " 'dataset_mapping.pkl',\n", + " 'nuscenes_0',\n", + " 'nuscenes_5',\n", + " 'nuscenes_7',\n", + " 'nuscenes_1',\n", + " 'nuscenes_4',\n", + " 'nuscenes_2']" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "nuscenes_data = AssetLoader.file_path(AssetLoader.asset_path, \"nuscenes\", return_raw_style=False) # Use the built-in datasets with simulator\n", + "os.listdir(nuscenes_data) # there are 10 nuscenes scenario file with a 'dataset_summary.pkl' and a 'dataset_summary.pkl'" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "\n", + "## Read Data Easily with Scenario Description\n", + "\n", + "A established ScenarioNet dataset is a folder containing `dataset_mapping.pkl` and `dataset_summary.pkl`. `dataset_mapping.pkl` contains the mapping from the scenario ID to the relative path. `dataset_summary.pkl` summarizes the meta information for each scenario.\n", + "\n", + "You can find that for the Waymo dataset, as we put all scenarios in the same folder, we don't need the `dataset_mapping.pkl` that routes scenario ID to corresponding `.pkl` file. In the nuScenes dataset, we have both `dataset_mapping.pkl` and `dataset_summary.pkl` because we have a hierachy file structure to store the scenarios.\n", + "\n", + "In this section, we demonstrate how to use the utilities from MetaDrive to easily access scenarios.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "from scenarionet import read_dataset_summary, read_scenario" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Read the dataset summary" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "\u001b[0;31mSignature:\u001b[0m \u001b[0mread_dataset_summary\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdataset_path\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mDocstring:\u001b[0m\n", + "Read the dataset and return the metadata of each scenario in this dataset.\n", + "\n", + "Args:\n", + " dataset_path: the path to the root folder of your dataset.\n", + "\n", + "Returns:\n", + " A tuple of three elements:\n", + " 1) the summary dict mapping from scenario ID to its metadata,\n", + " 2) the list of all scenarios IDs, and\n", + " 3) a dict mapping from scenario IDs to the folder that hosts their files.\n", + "\u001b[0;31mFile:\u001b[0m ~/scenarionet/scenarionet/common_utils.py\n", + "\u001b[0;31mType:\u001b[0m function" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "read_dataset_summary?" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "dataset_summary, scenario_ids, mapping = read_dataset_summary(dataset_path=waymo_data)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "`dataset_summary` is the summary dict mapping from scenario ID to its metadata." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + 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19.261213660240173,\n", + " 'valid_length': 11,\n", + " 'continuous_valid_length': 11},\n", + " '915': {'type': 'VEHICLE',\n", + " 'object_id': '915',\n", + " 'track_length': 91,\n", + " 'distance': 3.597263425588608,\n", + " 'valid_length': 7,\n", + " 'continuous_valid_length': 6},\n", + " '916': {'type': 'VEHICLE',\n", + " 'object_id': '916',\n", + " 'track_length': 91,\n", + " 'distance': 6.829868257045746,\n", + " 'valid_length': 8,\n", + " 'continuous_valid_length': 8},\n", + " '917': {'type': 'VEHICLE',\n", + " 'object_id': '917',\n", + " 'track_length': 91,\n", + " 'distance': 2.040453091263771,\n", + " 'valid_length': 4,\n", + " 'continuous_valid_length': 4},\n", + " '918': {'type': 'VEHICLE',\n", + " 'object_id': '918',\n", + " 'track_length': 91,\n", + " 'distance': 0.0,\n", + " 'valid_length': 1,\n", + " 'continuous_valid_length': 1},\n", + " '919': {'type': 'VEHICLE',\n", + " 'object_id': '919',\n", + " 'track_length': 91,\n", + " 'distance': 0.0,\n", + " 'valid_length': 1,\n", + " 'continuous_valid_length': 1},\n", + " '924': {'type': 'VEHICLE',\n", + " 'object_id': '924',\n", + " 'track_length': 91,\n", + " 'distance': 0.0,\n", + " 'valid_length': 5,\n", + " 'continuous_valid_length': 5},\n", + " '1101': {'type': 'VEHICLE',\n", + " 'object_id': '1101',\n", + " 'track_length': 91,\n", + " 'distance': 122.12530386447906,\n", + " 'valid_length': 91,\n", + " 'continuous_valid_length': 91}},\n", + " 'number_summary': {'object': 115,\n", + " 'dynamic_object_states': 19,\n", + " 'map_features': 640,\n", + " 'object_types': {'CYCLIST', 'PEDESTRIAN', 'VEHICLE'},\n", + " 'object_types_counter': {'PEDESTRIAN': 4, 'CYCLIST': 1, 'VEHICLE': 110},\n", + " 'dynamic_object_states_types': {'LANE_STATE_ARROW_CAUTION',\n", + " 'LANE_STATE_ARROW_STOP',\n", + " 'LANE_STATE_GO',\n", + " 'LANE_STATE_STOP',\n", + " 'LANE_STATE_UNKNOWN'},\n", + " 'dynamic_object_states_counter': {'LANE_STATE_GO': 420,\n", + " 'LANE_STATE_ARROW_CAUTION': 24,\n", + " 'LANE_STATE_ARROW_STOP': 402,\n", + " 'LANE_STATE_UNKNOWN': 63,\n", + " 'LANE_STATE_STOP': 84}}}}" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dataset_summary" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "`scenario_ids` is the list of all scenarios IDs." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['sd_training.tfrecord-00000-of-01000_2a1e44d405a6833f.pkl',\n", + " 'sd_training.tfrecord-00000-of-01000_c403d5992cab9e0.pkl',\n", + " 'sd_training.tfrecord-00000-of-01000_8a346109094cd5aa.pkl']" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "scenario_ids" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "`mapping` is a dict mapping from scenario IDs to the folder that hosts their files." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'sd_training.tfrecord-00000-of-01000_2a1e44d405a6833f.pkl': '',\n", + " 'sd_training.tfrecord-00000-of-01000_c403d5992cab9e0.pkl': '',\n", + " 'sd_training.tfrecord-00000-of-01000_8a346109094cd5aa.pkl': ''}" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "mapping" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Read specified scenario\n", + "\n", + "`read_scenario` will return the ScenarioNet instance. Please refer to the ScenarioDescription class in MetaDrive for more information." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "\u001b[0;31mSignature:\u001b[0m \u001b[0mread_scenario\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdataset_path\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmapping\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mscenario_file_name\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mDocstring:\u001b[0m\n", + "Read a scenario pkl file and return the Scenario Description instance.\n", + "\n", + "Args:\n", + " dataset_path: the path to the root folder of your dataset.\n", + " mapping: the dict mapping return from read_dataset_summary.\n", + " scenario_file_name: the file name to a scenario file, should end with `.pkl`.\n", + "\n", + "Returns:\n", + " The Scenario Description instance of that scenario.\n", + "\u001b[0;31mFile:\u001b[0m ~/scenarionet/scenarionet/common_utils.py\n", + "\u001b[0;31mType:\u001b[0m function" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "read_scenario?" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "scenario_file_name = scenario_ids[0]\n", + "\n", + "scenario = read_scenario(dataset_path=waymo_data, mapping=mapping, scenario_file_name=scenario_file_name)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "metadrive.scenario.scenario_description.ScenarioDescription" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "type(scenario)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "dict_keys(['id', 'version', 'length', 'tracks', 'dynamic_map_states', 'map_features', 'metadata'])" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "scenario.keys()" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "scenario.sanity_check(scenario) # Pass check if no error is raised." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "scenario.to_dict()" + ] + } + ], + "metadata": { + "colab": { + "collapsed_sections": [], + "provenance": [] + }, + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.18" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +}